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tips:comp_two_independent_estimates

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tips:comp_two_independent_estimates [2019/09/26 07:15] – external edit 127.0.0.1tips:comp_two_independent_estimates [2020/07/03 10:23] – Wolfgang Viechtbauer
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 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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-While we find that studies using random assignment obtain larger (more negative) effects than studies not using random assignment ($b_1 = -0.490$, $SE = 0.351$), the difference between the two estimates is not significant ($z = -1.395$, $p = .163$).+While we find that studies using random assignment obtain on average larger (i.e., more negative) effects than studies not using random assignment ($b_1 = -0.490$, $SE = 0.351$), the difference between the two estimates is not significant ($z = -1.395$, $p = .163$).
  
 The test of the difference between the two estimates is really just a Wald-type test, given by the equation $$z = \frac{\hat{\mu}_1 - \hat{\mu}_2}{\sqrt{SE[\hat{\mu}_1]^2 + SE[\hat{\mu}_2]^2}},$$ where $\hat{\mu}_1$ and $\hat{\mu}_2$ are the two estimates and $SE[\hat{\mu}_1]$ and $SE[\hat{\mu}_2]$ the corresponding standard errors. The test statistics can therefore also be computed with: The test of the difference between the two estimates is really just a Wald-type test, given by the equation $$z = \frac{\hat{\mu}_1 - \hat{\mu}_2}{\sqrt{SE[\hat{\mu}_1]^2 + SE[\hat{\mu}_2]^2}},$$ where $\hat{\mu}_1$ and $\hat{\mu}_2$ are the two estimates and $SE[\hat{\mu}_1]$ and $SE[\hat{\mu}_2]$ the corresponding standard errors. The test statistics can therefore also be computed with:
tips/comp_two_independent_estimates.txt · Last modified: 2024/04/18 11:36 by Wolfgang Viechtbauer